Spatial distribution and its limiting environmental factors of native orchid species diversity in the Beipan River Basin of Guizhou Province, China

Abstract To understand the distribution of biodiversity and its determinants, particularly that of ecologically sensitive ones, has long been intriguing to the science community and will help formulate conservation strategies under future climate changes. To this end, we conducted extensive field surveys on the distribution of orchid flora in the Beipan River Basin in Guizhou Province, which is one of the biodiversity conservation priorities in China. The data we acquired, together with those published previously, were converted into orchid species richness for each of the 3 km × 3 km grid cells covering the study region. Redundancy analysis (RDA) and geographically weighted regression (GWR) were then applied to determine which of the 30 environmental factors are potentially critical for the spatial distribution of orchid flora we have observed. Despite a moderate spatial extent, we found that the Beipan River Basin harbors about 249 native orchid species belonging to 74 genera, equivalent to 14.5% of orchid flora of China. Orchid species richness in this area follows a descending gradient from the southeast to the northwest, 70.41% of its variation among grid cells can be explained by environmental factors and spatial variables, and spatial variables accounted for 63.90% of the spatial variation of orchid distribution, indicating that spatial variables played a dominant role in the distribution of wild Orchidaceae species richness. In addition, the main environmental driver is the mean temperature of the wettest quarter. Our study provides a good example for revealing the main drivers of orchid distribution characteristics and has a certain reference value for the development of orchid conservation strategies.


| INTRODUC TI ON
With about 28,000 species and 736 genera recognized to date (Christenhusz & Byng, 2016), orchid is among the most evolved, and diverse plant families. Species in this family are characterized by the highly specialized structure of their flowers (Kong et al., 2012), many of which are showy. Unfortunately, over-collection for their medicinal and ornamental value, as well as their inherent ecological sensitivities (Yin et al., 2018), has rendered many orchid species threatened by extinction (Swarts & Dixon, 2009). In addition, because they are widely distributed and occur in a wide spectrum of habitats, this makes orchids an important group for biological conservation and an ideal group for exploring species distribution models (Tsiftsis et al., 2019).
Southwest China is one of the world-renowned biodiversity hotspots (Myers et al., 2000). This region is also extremely rich in orchid diversity (Dixon et al., 2003). Located in southwest China, Guizhou Province is characterized by well-developed karst landforms, which cover 61.9% of its total land area. The Beipan River Basin is located in the southwest of Guizhou Province. Despite its moderate spatial extent, the Beipan River Basin is a major intersection of the southwest and southeast Asian monsoon and is also the hinterland with the most obvious and developed karst geological structure in Guizhou Province. Prominent environmental heterogeneity and desirable vapor availability in the Beipan River Basin produce favorable habitats for orchid species. It is, therefore, an ideal place for investigating the spatial distribution of orchid flora and unraveling dominant environmental factors underlying such distribution.
The present distribution of biodiversity results from long-term interaction among organisms, or between organisms and the abiotic environment (Sterner et al., 1986). Because organisms vary in their responses to these biotic or abiotic interactions, different patterns of distribution may arise. Since the 18th century, dozens of hypotheses have been purposed to explain the spatial distribution of biodiversity, such as the water-energy dynamic hypothesis (O'Brien, 2006), ambient energy hypothesis (Wright, 1983), and habitat heterogeneity hypothesis (MacArthur & MacArthur, 1961), and new hypotheses are emerging (Wang et al., 2012). In general, these hypotheses pertain to two broad categories of factors, i.e., present environmental conditions and historical contingency. Environmental conditions are the most important determinants of the distribution of biodiversity on Earth, among which water, energy, and habitat heterogeneity are particularly decisive for plant species richness (Hawkins & Porter, 2010;Wang et al., 2018). Moreover, spatial variables are also important factors influencing the distribution of plant diversity (La et al., 2016), and should be taken into account by relevant studies.
To date, studies on the spatial distribution of orchids, as well as underlying drivers, are relatively scant. Such studies are often based on specimens or floras (Acharya et al., 2011;Phillips et al., 2011), resulting in potential bias due to incomplete species inventories or even incorrect taxon identification. Therefore, it is of great significance to analyze the distribution patterns of orchid species in the study area based on field survey data and to determine the factors underlying such observed patterns of orchid diversity.

| Species survey
Orchidaceae spatial distribution data used in this study were derived from a comprehensive dataset that included both field-based species distribution records and those available in published literatures and floras. Intensive field survey covered the entire study area ( Figure 1) and was particularly concentrated in places where historical investigations indicated high orchid diversity, as well as where field survey had not been conducted adequately. We established transects with a length of at least 100 m and set up quadrats (5 m * 5 m) on both sides of each transect. A minimal distance of 10 m was kept between any neighboring quadrats. For each quadrat, we recorded the GPS location, elevation, slope, aspect, orchid species composition (including abundance of each species and abundance of breeding individuals of each species), characteristics of local vegetation (e.g., foundational species), characteristics of local abiotic environments (e.g., type of soil or bedrock), and level of anthropogenic disturbance.

| Environmental variables
To identify environmental variables that can explain the observed spatial distribution of native orchids in the Beipan River Basin, we first downloaded a total of 30 environmental factors categorized into four types: energy, water, habitat heterogeneity, and human activities, covering most of those known to have large impacts on the distribution of terrestrial plant diversity.

| Habitat heterogeneity variables
We selected 4 variables that quantify habitat heterogeneity, including elevational range (ER), the number of vegetation formations (NVF) (Ran et al., 2012), and ranges of MAT and MAP (RMAT and RMAP, respectively) for each grid. ER was extracted from a 12.5 m digital elevation model (DEM) (https://search.asf.alaska.edu/), and NVF with the resolution of 1 km 2 from the Center for Resources and Environmental Science and Data, Chinese Academy of Sciences (http://resdc.cn/).

| Human activities variables
We selected 3 variables as proxies of human activity intensity, in- To keep the spatial resolution consistent, the DEM data are resampled to 1 km * 1 km.

| Statistical analysis
The study area was divided into 3 km × 3 km grid cells, and the grid cells at the boundaries with more than 1/3 of its area falling out of the study area were excluded, after which 3499 grid cells were retained. Values of energy, water, habitat heterogeneity, and human activity variables for each grid cell was computed as the mean value characterizing the spatial area covered by that grid cell. Then, we also computed orchid species richness for each grid cell. Because it is nearly impossible, within our capacity, to carry out an extensive field survey in each grid cell, this was done in two steps. First, an inventory of orchid species, including the elevational range of each species, was made for each county within the study area, based on our field-collected data and those available in floras and published literatures. Then, the orchid species richness of each grid cell was computed as the number of orchid species present in the county F I G U R E 1 Schematic diagram of investigation sites for Orchidaceae that the grid cell falls in, and filtered by the elevational range of each species. Note that when a grid cell falls at the adjoining area of >1 counties, it is assumed to fall within the county, which contains the largest proportion of its area ( Figure 2).

| Linear regression
Linear regressions were made in R3.6.3 (https://www.R-proje ct.org/) (R Core Team, 2019) to determine the relationship between orchid species richness and each of the environmental variables (including longitude, latitude, and altitude).

| Redundancy analysis
Based on the geographical coordinates of the centroid of each grid, a distance-based Moran's eigenvector map (dbMEM) is generated to create spatial variables, and a two-step procedure is used to forward the selection of spatial variables and environmental variables.
We used these selected variables to explain the spatial distribution of orchid species richness in the study area by using redundancy analysis (RDA) and variance partitioning (Borcard et al., 1992;Smith & Lundholm, 2010), so as to identify the contribution of these variables in shaping the spatial pattern of orchid diversity we have observed.

| Principal component analysis
In order to deal with multicollinearity among environmental variables, principal component analysis (PCA) was performed for each of the four groups of environmental variables (energy, water, habitat heterogeneity, and human activity).

| Geographically weighted regression
Considering the spatial location change of the regression coefficient, geographical coordinates are included as parts of the regression parameters. We performed spatial autocorrelation tests on variables in ArcGIS 10.6 for the analysis of geographical weighted regression (GWR). GWR is an effective method for determining spatially variable parameters by taking into account local-scale features of geographical elements. The GWR, as proposed by Brunsdon et al. (Brunsdon et al., 1996), is computed as: We used Akaike's Information Criterion (AIC) (Akaike, 1974), which takes into account model complexity and goodness-of-fit, to evaluate the OLS and GWR models. A more desirable model is indicated by a smaller AIC. Altogether 260 transects were established, with 2493 quadrats with orchid occurrence set and recorded.
These field-collected data, in combination with our extensive literature review, documented 249 native orchid species belonging to 74 genera in the Beipan River Basin (Extended Data Table S1). These account for 14.5% and 41.9% of the species and genera of native orchids recorded in China to date, despite a relatively small spatial extent (0.2%) of this area.

| Distribution characteristics of species richness and environmental factors
Consistent with the remarkable environmental heterogeneity (Table 1), the spatial distribution of native orchids is also highly uneven across our study area (Figure 3). There is an extremely significant quadratic correlation (p < .001) between orchid species richness and each of longitude, latitude, and elevation.  Table S2). in Figure 4a.

F I G U R E 6 Pearson correlation among environmental factors
Four significant environmental variables, when in combination with spatial variables, jointly accounted for 70.41% variation in the distribution of native orchid species in the Beipan River Basin.
The proportions of variation explained by them separately and their interaction are presented in Figure 4b. The results indicated that both the spatial structure of species richness (variation explained solely by spatial variables) and the spatial structure of environmental variables (variation explained by the interaction of spatial and environmental variables) have huge impacts on the spatial distribution of orchid species in our study area. Therefore, the GWR model was employed to analyze the effect of environmental variables on the distribution of orchid species richness at finer spatial scales.

| Multicollinearity issues and principal component analysis among environmental variables
Because the correlation between multiple environmental factors was above 0.8, and the significance level was extremely significant (p < .001) ( Figure 6). Principal component analysis of environmental factors showed that the cumulative proportions of original variance retained by the first two axes (hereafter "principal environmental variables") were 87.52% for energy variables, 83.67% for water variables, 83.68% for habitat heterogeneity-related variables, and 80.72% for human activity-related variables, respectively ( Table 2).
The scores of these eight principal axes served as candidate variables for performing GWRs below. Note: Since >80% of original variations is retained by the first two axes for each of the four groups, we use them (eight principal components in total) as candidate explanatory variables for geographical weighted regressions, so as to preserve as much original variation as possible, while avoiding multicollinearity issues. a Principal components are listed by the proportion of original variations they retain.
Since two of these correlations (those pertaining to SF2 and SJ2) were nonsignificant with p > .05, we excluded them and used the remaining six principal environmental variables to perform GWR.
All variance inflation factors (VIF) associated with these remaining ones fell below the threshold of 7.5, indicating negligible issues of multicollinearity.
The GWR model was able to explain 70.08% of the variance of spatial distribution of orchid species richness in the Beipan River Basin, indicating its marked superiority over the OLS model, which explained only 26.81% (Table 4). Such relative advantage of GWR is also corroborated by other goodness-of-fit indices such as adjusted R 2 and values of small sample-corrected Akaike Information Criterion (AICc). We therefore believe that GWR is a considerably more effective approach for analyzing the role of environmental variables in shaping the spatial distribution of orchid species in our study region.

| Effects of principal environmental variables
According to the results of GWR (Figure 7), the effects of the six principal environmental variables on the species richness of native orchids in the Beipan River Basin all displayed substantial spatial heterogeneity and even produced opposite effects across the study area. The effect of SJ1 on orchid species richness was dominated by the number of stimulatory grids (97.26%), on the contrary, NL1 (73.91%) and RL1 (74.96%) showed the predominance of inhibitory grids. In addition, NL2 (57.65%), SF1 (53.39%), and RL2 (50.56%) showed that the grid numbers of facilitation and inhibition were relatively close.

| DISCUSS ION
There is significant spatial heterogeneity in the distribution of species richness, which varies with the longitude, latitude, or altitude span of the study area (Rahbek, 2005). Studies have shown that the importance of water, relative to energy, in determining terrestrial plant diversity, varies along the latitudinal gradient. Water plays a primary role in low-latitude regions, while energy is the more dominant one affecting the geographical pattern of species richness in high latitudes (Eiserhardt et al., 2011;Kreft & Jetz, 2007;Oliveira & Diniz-Filho, 2010;Xu et al., 2013). In this study, the species richness of orchids first decreased and then increased with longitude, which can be explained by the degraded habitat quality, sore desertification, cattle grazing, and anthropogenic excavation prevailing in the central part of our study area (Chen et al., 2016;Peng et al., 2013). It shows a monotonous decreasing trend with increasing latitude and altitude, which may be related to the downstream area of Beipan River in Guizhou province is located mainly in lower latitudes, with lower elevation accompanied by stretching mountains and desirable humidity and heat, allowing native orchid flora to thrive.
Spatial variables were able to account for 63.90% of the spatial variation of orchid distribution, much higher than the proportion explained by environmental variables (40.00%). This indicates that spatial structure played an important role in shaping the spatial distribution of orchid species richness in Beipan River of Guizhou province. Consistent with Gravel et al. (2006), who argue that niche differentiation plays a dominant role in the distribution of  (Borcard et al., 2018). The proportion of spatial variation explained solely by spatial variables was 30.41%, which is often deemed to derive mainly from the spatial autocorrelation of species distribution (La et al., 2016). Nonetheless, this part of the variation in orchid distribution may also arise from historical geological events (Svenning & Skov, 2010), diffusion limitations (Gilbert & Lechowicz, 2004), or other unidentified environmental factors (Smith & Lundholm, 2010).
Despite that an array of environmental variables have been selected, 29.59% variance of the spatial distribution of native orchid flora in our study area remained unexplained. This may be due to the lack of information about the symbiotic fungi which orchids rely on to thrive. The occurrence of orchids is generally sensitive to the ambient environment, and texture, water content, as well as physical and chemical properties of the sediments, are all among the critical factors determining the existence of orchid flora (Li et al., 2009). In addition, all orchids known to date must rely on symbiotic fungi for nutrition at least during their early life stages (Arditti & Ghani, 2000). Therefore, soil properties and the presence of eligible microorganisms may as well serve as major factors affecting the spatial pattern of species richness of wild orchids in There is significant spatial heterogeneity in each environmental factor affecting the species richness of orchids. Although there is abundant rainfall in the study area, the karst landforms are welldeveloped and their ability to retain water is weak. Therefore, the effect of water factor 1 (SF1) on species richness of orchids is dominant in the promoting grid number, while that of SF1 was dominant in the inhibiting grid number in the non-karst region. However, the effect of habitat heterogeneity factor 1 (SJ1) on the species richness of orchids in the study area is dominated by the number of grids that promote the effect, that is, there is a significant positive correlation between SJ1 and orchid richness. Therefore, the diversity of orchids within a certain area was argued to serve, to some extent, as a proxy of overall biodiversity (Jin et al., 2011).

| CON CLUS ION
The area of the Beipan River basin in Guizhou accounts for about 11.88% of the land area of Guizhou Province, but 80.43% of the genera and 72.59% of the species of wild orchids in Guizhou Province are distributed. It is one of the most abundant areas of wild orchids in Guizhou Province. In general, the species richness of wild orchids in Beipan River showed a pattern of "high in southeast and low in northwest, and east-west differentiation," which was influenced by orchids has been carried out in succession.

ACK N OWLED G M ENTS
We are grateful to Mr. Hanchen Wang for his valuable comments and for helping to improve the English writing on this manuscript.
We also thank the editor and three referees who provided helpful

CO N FLI C T O F I NTE R E S T
The authors declare that they have no conflict of interest.

DATA AVA I L A B I L I T Y S TAT E M E N T
Data associated with this manuscript can be accessed at the Dryad data repository (https://doi.org/10.5061/dryad.k0p2n gfbx).